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An Improved ACO with First-job Restriction and P-ANT for Batch Scheduling Problem

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An Improved ACO with First-job Restriction and P-ANT for Bat...

Symposia and Workshops on Ubiquitous, Autonomic and Trusted Computing, UIC-ATC

作者： Yang Yang Zhaohong Jia Yiwen Zhang Kai Li Key Lab of Intelligent Computing and Signal Processing of Ministry of Education School of Computer Science and Technology Anhui University Hefei China School of Management Hefei University of Technology Hefei China

ISBN: (数字)9781728140346

ISBN: (纸本)9781728140353

This research applies ant colony optimization (ACO) algorithm to minimizing the makespan and the maximum tardiness for scheduling the jobs with non-identical sizes and release time on parallel batch process machines. To improve search efficiency, a new first job selection strategy and a dynamic heuristic are provided. Additionally, a new role, called P-ANT, is designed to improve the search ability of the ant colony. To solve the studied problem, a novel multi-objective ACO algorithm with P-ANT (MACOP) is proposed. The MACOP is compared with several other state-of-the-art algorithms through extensive experiments. The results demonstrate the effectiveness of the proposed MACOP algorithm.

关键词： Heuristic algorithms Job shop scheduling Genetic algorithms signal processing algorithms Optimization Batch production systems Ant colony optimization

Improvement of the Human Action Recognition Algorithm by the Pre-processing of Input Data

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IOP Conference Series: Materials Science and Engineering 2021年第1期1029卷

作者： M M Zhdanova O S Balab.eva V V Voronin E A Semenishchev A A Zelensky Center for Cognitive Technology and Machine Vision Moscow State University of Technology «STANKIN» Moscow Russia Lab. «Mathematical methods of image processing and intelligent computer vision systems» Don State Technical University Rostov-on-Don Russia

The paper presents an approach to recognizing human actions using an additional preprocessing stage of input data. The growing volumes of video information do not always allow support the quality of data at a high level; this can cause limitations in the further processing of digital data. In this regard, it becomes urgent to introduce an additional stage of image enhancement into the algorithm for recognizing actions in video. The proposed method includes three main steps: image enhancement, constructing a descriptor, and classification. The presented image enhancement stage is based on the combined local and global image processing in the frequency domain. The basic idea in using local alfa-rooting method is to apply it to different disjoint blocks with different sizes. To solve the problem of constructing a descriptor, a three-dimensional microblock dense difference (3D DMD) algorithm is used, which provides a highly oriented representation of image regions by tightly capturing microblocks within each region in several orientations and scales. 3D DMD has several advantages over other methods: higher efficiency compared to existing methods; minimal computational costs when using an integrated image; low dimension; ease of implementation; does not require settings. The presented modification allows to increase productivity by 2-4%.

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RGB-T image saliency detection via collab.rative graph learning

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arXiv 2019年

作者： Tu, Zhengzheng Xia, Tian Li, Chenglong Wang, Xiaoxiao Ma, Yan Tang, Jin Key Lab of Intelligent Computing and Signal Processing of Ministry of Education School of Computer Science and Technology Anhui University Hefei China Institute of Physical Science and Information Technology Anhui University Hefei China

Image saliency detection is an active research topic in the community of computer vision and multimedia. Fusing complementary RGB and thermal infrared data has been proven to be effective for image saliency detection. In this paper, we propose an effective approach for RGB-T image saliency detection. Our approach relies on a novel collab.rative graph learning algorithm. In particular, we take superpixels as graph nodes, and collab.ratively use hierarchical deep features to jointly learn graph affinity and node saliency in a unified optimization framework. Moreover, we contribute a more challenging dataset for the purpose of RGB-T image saliency detection, which contains 1000 spatially aligned RGB-T image pairs and their ground truth annotations. Extensive experiments on the public dataset and the newly created dataset suggest that the proposed approach performs favorably against the state-of-the-art RGB-T saliency detection methods. Copyright © 2019, The Authors. All rights reserved.

关键词： Machine learning

Active Lighting Recurrence by Parallel Lighting Analogy for Fine-Grained Change Detection

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arXiv 2020年

作者： Zhang, Qian Feng, Wei Wan, Liang Tian, Fei-Peng Wang, Xiaowei Tan, Ping School of Computer Science and Technology College of Intelligence and Computing Tianjin University TianJin300350 China State Administration of Cultural Heritage China School of Computer Software College of Intelligence and Computing Tianjin University TianJin300350 China Tianjin Key Lab for Advanced Signal Processing Civil Aviation University of China Dunhuang Research Academy Dunhuang Gansu China School of Computing Science Simon Fraser University Canada

—This paper studies a new problem, namely active lighting recurrence (ALR) that physically relocalizes a light source to reproduce the lighting condition from single reference image for a same scene, which may suffer from fine-grained changes during twice observations. ALR is of great importance for fine-grained visual inspection and change detection, because some phenomena or minute changes can only be clearly observed under particular lighting conditions. Therefore, effective ALR should be able to online navigate a light source toward the target pose, which is challenging due to the complexity and diversity of real-world lighting and imaging processes. To this end, we propose to use the simple parallel lighting as an analogy model and based on Lambertian law to compose an instant navigation ball for this purpose. We theoretically prove the feasibility, i.e., equivalence and convergence, of this ALR approach for realistic near point light source and small near surface light source. Besides, we also theoretically prove the invariance of our ALR approach to the ambiguity of normal and lighting decomposition. The effectiveness and superiority of the proposed approach have been verified by both extensive quantitative experiments and challenging real-world tasks on fine-grained change detection of cultural heritages. We also validate the generality of our approach to non-Lambertian scenes. Copyright © 2020, The Authors. All rights reserved.

关键词： Light sources

Realization of remote sensing image segmentation based on K-means clustering

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Realization of remote sensing image segmentation based on K-...

第二届材料科学应用与能源材料国际研讨会

作者： Yanhong Wang Denghui Li Yuying Wang Guangxi Key Lab of Wireless Wideband Communication & Signal Processing Institute Of Information Technology of Guilin University of Electronic Technology Key Laboratory of Cognitive Radio and Information Processing Ministry of Education(Guilin University of Electronic Technology) GUET School of Computer Science and Information Security of GUET

Segmentation of remote sensing image is the key technology of positioning ***,we transform the remote sensing image from RGB pace to lab.***,three centres are iterated by using K-means ***,in order to eliminate the influence,the closed operation of mathematical morphology is used to correct the segmented *** results show that it can segment the road from the remote sensing image in lab.mode,by using K-means clustering ***,lab.mode is more suitable for k-mean than other modes.

关键词： Realization of remote sensing image segmentation based on K-means clustering image

Syntax-enhanced Pre-trained model

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arXiv 2020年

作者： Xu, Zenan Guo, Daya Tang, Duyu Su, Qinliang Shou, Linjun Gong, Ming Zhong, Wanjun Quan, Xiaojun Jiang, Daxin Duan, Nan School of Computer Science and Engineering Sun Yat-sen University Guangzhou China Microsoft Research Asia Beijing China Microsoft Search Technology Center Asia Beijing China Guangdong Key Laboratory Big Data Analysis and Processing Guangzhou China Key Lab. of Machine Intelligence and Advanced Computing Ministry of Education China

We study the problem of leveraging the syntactic structure of text to enhance pre-trained models such as BERT and RoBERTa. Existing methods utilize syntax of text either in the pre-training stage or in the fine-tuning stage, so that they suffer from discrepancy between the two stages. Such a problem would lead to the necessity of having human-annotated syntactic information, which limits the application of existing methods to broader scenarios. To address this, we present a model that utilizes the syntax of text in both pre-training and fine-tuning stages. Our model is based on Transformer with a syntax-aware attention layer that considers the dependency tree of the text. We further introduce a new pre-training task of predicting the syntactic distance among tokens in the dependency tree. We evaluate the model on three downstream tasks, including relation classification, entity typing, and question answering. Results show that our model achieves state-of-the-art performance on six public benchmark datasets. We have two major findings. First, we demonstrate that infusing automatically produced syntax of text improves pre-trained models. Second, global syntactic distances among tokens bring larger performance gains compared to local head relations between contiguous tokens. Copyright © 2020, The Authors. All rights reserved.

关键词： Syntactics

AIM 2020 Challenge on Image Extreme Inpainting 16th

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AIM 2020 Challenge on Image Extreme Inpainting

Workshops held at the 16th European Conference on computer Vision, ECCV 2020

作者： Ntavelis, Evangelos Romero, Andrés Bigdeli, Siavash Timofte, Radu Hui, Zheng Wang, Xiumei Gao, Xinbo Shin, Chajin Kim, Taeoh Son, Hanbin Lee, Sangyoun Li, Chao Li, Fu He, Dongliang Wen, Shilei Ding, Errui Bai, Mengmeng Li, Shuchen Zeng, Yu Lin, Zhe Yang, Jimei Zhang, Jianming Shechtman, Eli Lu, Huchuan Zeng, Weijian Ni, Haopeng Cai, Yiyang Li, Chenghua Xu, Dejia Wu, Haoning Han, Yu Nadim, Uddin S. M. Jang, Hae Woong Ahmed, Soikat Hasan Yoon, Jungmin Jung, Yong Ju Li, Chu-Tak Liu, Zhi-Song Wang, Li-Wen Siu, Wan-Chi Lun, Daniel P. K. Suin, Maitreya Purohit, Kuldeep Rajagopalan, A.N. Narang, Pratik Mandal, Murari Chauhan, Pranjal Singh Computer Vision Lab ETH Zürich Zürich Switzerland CSEM Neuchâtel Switzerland School of Electronic Engineering Xidian University Xi’an China Image and Video Pattern Recognition Laboratory School of Electrical and Electronic Engineering Yonsei University Seoul Korea Republic of Baidu Inc. Beijing China Beijing China Dalian University of Technology Dalian China Adobe San Jose United States Rensselaer Polytechnic Institute Troy United States Peking University Beijing China Lab Gachon University Seongnam Korea Republic of Centre for Multimedia Signal Processing Department of Electronic and Information Engineering The Hong Kong Polytechnic University Hong Kong China Indian Institute of Technology Madras Chennai India BITS Pilani Pilani India MNIT Jaipur Jaipur India

ISBN: (纸本)9783030670696

This paper reviews the AIM 2020 challenge on extreme image inpainting. This report focuses on proposed solutions and results for two different tracks on extreme image inpainting: classical image inpainting and semantically guided image inpainting. The goal of track 1 is to inpaint large part of the image with no supervision. Similarly, the goal of track 2 is to inpaint the image by having access to the entire semantic segmentation map of the input. The challenge had 88 and 74 participants, respectively. 11 and 6 teams competed in the final phase of the challenge, respectively. This report gauges current solutions and set a benchmark for future extreme image inpainting methods. © 2020, Springer Nature Switzerland AG.

关键词： Semantic Segmentation

Design and Implementation of a Course Answering System Based on Error Correction and Trie Tree Language Model

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Journal of Physics: Conference Series 2020年第1期1621卷

作者： Danyang Li Yahui Zhao Mengjiang Luo Rongyi Cui Intelligent Information Processing Lab. Dept. of Computer Science & Technology Yanbian University Yanji 133002 China

With the rapid growth of network information, the accuracy of input text affects the retrieval results, text proofreading technology emerges as the times require in order to avoid the situation of 'wrong answers' caused by wrong questions when searching for information. In the course question answering system, we also need to consider the query speed and efficiency. In order to avoid the trouble of manual proofreading, this paper proposes a system which can automatically correct wrong questions in the professional field of curriculum. Firstly, used the edit distance method for fuzzy matching of error strings; then, used trie tree language model to store data to improve query efficiency. Finally, compared the proofreading effect under different text similarity thresholds, and selected the best value for the experiment. After experimental analysis and comparison, the best result is selected when the text similarity is 0.5, the accuracy rate is 77.91%, the recall rate is 67%, and the F value is 72.04%. Experiments show that the system designed in this paper can effectively correct the wrong text in the field of computer.

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Edge-guided non-local fully convolutional network for salient object detection

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arXiv 2019年

作者： Tu, Zhengzheng Ma, Yan Li, Chenglong Tang, Jin Luo, Bin Key Lab of Intelligent Computing and Signal Processing of Ministry of Education School of Computer Science and Technology Anhui University Hefei230601 China Institute of Physical Science and Information Technology Anhui University Hefei230601 China

Fully Convolutional Neural Network (FCN) has been widely applied to salient object detection recently by virtue of high-level semantic feature extraction, but existing FCN-based methods still suffer from continuous striding and pooling operations leading to loss of spatial structure and blurred edges. To maintain the clear edge structure of salient objects, we propose a novel Edge-guided Non-local FCN (ENFNet) to perform edge-guided feature learning for accurate salient object detection. In a specific, we extract hierarchical global and local information in FCN to incorporate non-local features for effective feature representations. To preserve good boundaries of salient objects, we propose a guidance block to embed edge prior knowledge into hierarchical feature maps. The guidance block not only performs feature-wise manipulation but also spatial-wise transformation for effective edge embeddings. Our model is trained on the MSRA-B dataset and tested on five popular benchmark datasets. Comparing with the state-of-the-art methods, the proposed method achieves the best performance on all datasets. Copyright © 2019, The Authors. All rights reserved.

关键词： Object detection